Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 22 for “"Likelihood functions"”.
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Semi-Parametric Likelihood Functions for Bivariate Survival Data
… the two random variables and a nonparametric likelihood function for the unknown random variable. Associated properties are studied and investigated and applications with simulated and real data are given.</p>
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Architectures for Symbol Timing Synchronization in MIMO Communications
Maximum likelihood symbol timing estimation for communication over a frequency non-selective MIMO fading channel is developed. The cases of known data (data-aided estimation) and unknown data (non-data-aided estimation) together with known channel and unknown channel are considered. The analysis …
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A Novel Penalized Log-likelihood Function for Class Imbalance Problem
<p>The log-likelihood function is the optimization objective in the maximum likelihood method for estimating models (e.g., logistic regression, neural network). However, its formulation is based on assumptions that the target classes are equally distributed and the overall accuracy is maximized, …
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Likelihood estimation for jointly analyzing item responses and response times
… and response time models by maximizing the likelihood functions. A series of simulation studies verify that the estimation methods perform appropriately, and the parameters are robustly estimated. The likelihood-based approach provides a practical and efficient alternative to Bayesian …
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Line transect abundance estimation with uncertain detection on the trackline
… line transect estimation theory to date, general likelihood functions are derived for the case in which detection probabilities are modelled as functions of any number of explanatory variables and detection of animals on the trackline (i.e. directly in the observer's path) is not certain. Existing …
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Information theoretic design of breast sonography
… features are expressed mathematically as likelihood functions. Realistic approximations to the ideal strategy for each task are proposed as an additional beamforming procedure to maximize diagnostic image information content available to readers. Our previous study revealed that the Wiener …
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Model Uncertainties and Joint Inversion in Geophysics
… different types of data uses oversimpli ed likelihood models, which can be interpreted as being induced by white noise, zero mean and uncorrelated with the unknowns. The problem is that the use of these trivial likelihood models tends to underestimate the underlying uncertainties of the …
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Development of an Aggregation Methodology for Risk Analysis in Aerospace Conceptual Vehicle Design
… of uncertainty assessments in environments where likelihood functions and empirically assessed expert credibility factors are deficient is possible. Validation of the methodology provides evidence that decision-makers find the aggregated responses useful in formulating decision strategies.</p>
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Methods for checking the goodness of fit of alternative nonlinear mixed models with an application in fertility traits of beef cows
… the analysis of deviance using posterior density functions associated with alternative models, rather than likelihood functions. The major problem with the statistic generated here (referred hereafter as STAT) resides in calculating the integration constants exactly. To avoid this problem a …
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Scaling Cooperative Intelligence via Inverse Planning and Probabilistic Programming
… communicative cooperation. By using LLMs as likelihood functions within probabilistic programs, CLIPS can infer human goals from ambiguous instructions, then provide uncertainty-aware assistance with much higher levels of reliability than LLMs can on their own. In addition, CLIPS can be used …
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Analysis of Coding Region SNPs and Its Propensity to Cause Disease
Single Nucleotide Polymorphisms or (SNPs) are the most abundant form of variation present in the human genome. These variations in individuals are considered to be the cause of diseases, difference in response to treatment, susceptibility to diseases or may have no impact. Association studies aim …
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The Molecular Systematics of the Side-blotched Lizards (IGUANIA: PHRYNOSOMATIDAE: UTA)
… represented. Both maximum parsimony and maximum likelihood were used to reconstruct phylogenetic relationships. This study is intended to expand on the analysis of Upton and Murphy (1997) who used 21 individuals representing five of the nine species. The deep divergence discovered by Upton and …
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Estimating Uncertainty in HSPF based Water Quality Model: Application of Monte-Carlo Based Techniques
… MC, two-phase MC, Generalized Likelihood Uncertainty Estimation (GLUE), and Markov Chain Monte Carlo (MCMC) —were applied to a Hydrological Simulation Program–FORTRAN (HSPF) model developed for the Mossy Creek bacterial TMDL in Virginia. Predictive uncertainty in percent …
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Optimal parameter adaptive estimation of stochastic processes
… Wiener measure. By using the recent results on likelihood functions, an expression for the <i>a posteriori</i> probability is found in terms of the conditioned estimates. In this connection, an extended version of the partition theorem of parameter adaptive estimation is proved. The unique …
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Some Problems Concerning the Generalized Hyperbolic and Related Distributions
… algorithm. It is well-known that the log-likelihood functions of these distributions are at and that they need \good" starting values to converge to an optimal value. These problems were reported in the literature even when sample sizes of 500 observations were used. Here, the approaches …
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Mixed Effects Modeling and Correlation Structure Selection for High Dimensional Correlated Data
… effects using conditional quadratic inference functions. The new approach does not require any specification of the likelihood functions. It can also accommodate serial correlation between observations within the same cluster, in addition to mixed-effects modeling. Other advantages include not …
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Statistical and Algorithmic Thresholds in Spin Glasses
… and often highly non-convex cost or log-likelihood functions, making them an excellent testing ground for such questions. Part I of this thesis studies statistical properties of these models. Chapter 2 identifies the storage capacity of the Ising perceptron, a simple model of a neural …
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Frequentist-Bayesian Hybrid Tests in Semi-parametric and Non-parametric Models with Low/High-Dimensional Covariate
… significant differences between non-parametric functions and the second one is to design a test allowing any departure of predictors of high dimensional X from constant. The implementation is also given in construction of the proposal test statistics for both problems. For the first testing …
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Essays on testing spatial models
… a time lag and a spatial-time lag. The maximum likelihood estimator for the estimation of SDPD models can have asymptotic bias because of individual and time fixed effects. Bias arises since the limiting distributions of the score functions derived from the corresponding concentrated …
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Decoding algorithms for continuous phase modulation
… to include M-ary CPFSK. These receivers evaluate likelihood functions after observing the received signal for a certain number of symbol intervals, say N, then calculate a set of likelihood parameters on which a likelihood ratio test regarding the first symbol is based. These receivers are complex …
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